Multi-label learning studies the problem where each example is represented by a single
instance while associated with a set of labels simultaneously. During the past decade …

Multi‐label learning is quite a recent supervised learning paradigm. Owing to its capabilities
to improve performance in problems where a pattern may have more than one associated …

Multi-label classification deals with the problem where each instance is associated with
multiple class labels. Because evaluation in multi-label classification is more complicated …

Most of the multi-label classification (MLC) methods proposed in recent years intended to
exploit, in one way or the other, dependencies between the class labels. Comparing to …

We consider a hypercube view to perceive the label space of multilabel classification
problems geometrically. The view allows us not only to unify many existing multilabel …

The use of multi-label classification, ie, assigning unseen patterns to multiple categories,
has emerged in modern applications. A genetic-algorithm based multi-label feature …

Several meta-learning techniques for multi-label classification (MLC), such as chaining and
stacking, have already been proposed in the literature, mostly aimed at improving predictive …

Multi-label feature selection involves selecting important features from multi-label data sets.
This can be achieved by ranking features based on their importance and then selecting the …

Antimicrobial resistance (AMR) is a global health and development threat. In particular, multi-
drug resistance (MDR) is increasingly common in pathogenic bacteria. It has become a …

Multi-label learning is a challenging task in data mining which has attracted growing
attention in recent years. Despite the fact that many multi-label datasets have continuous …